Determining geographic locations for place names in a fact repository

TECHNICAL FIELD

The disclosed embodiments relate generally to analyzing place names extracted in a collection of documents. More particularly, the disclosed embodiments relate to analyzing place names that have been extracted from documents such as web pages.

BACKGROUND

Place names extracted from different sources have a variety of formats and may contain typographical errors, omissions, or unclear language. There may also be ambiguity as to whether a word represents a place name and whether different place names represent the same location. It is useful to have a way to identify the precise location of a place name.

SUMMARY

In accordance with one aspect of the invention, a computer-implemented method and computer program product process a text string within an object stored in memory to identify a first potential place name. The method and computer program product determine whether geographic location coordinates are known for the first potential place name. Further, the method and computer program product identify the first potential place name as a place name and tag the identified place name associated with an object in the memory with its geographic location coordinates, when the geographic location coordinates for the first identified place name are known.

In one embodiment of the invention, a system includes a potential place name identifier to determine if a text string contains a first potential place name. The system also includes a coordinate determiner to determine whether geographic location coordinates are known for the first potential place name. In addition, the system includes a place name identifier to determine whether the first potential place name is a place name and a coordinate assignor to tag the first identified place name associated with an object in the memory with its geographic location coordinates, when the geographic location coordinates for the first identified place name are known.

BRIEF DESCRIPTION OF THE DRAWINGS

<figref idrefs="DRAWINGS">FIG. 1</figref> shows a network, in accordance with a preferred embodiment of the invention.

<figref idrefs="DRAWINGS">FIGS. 2(</figref><i>a</i>)-<b>2</b>(<i>d</i>) are block diagrams illustrating a data structure for facts within a repository of <figref idrefs="DRAWINGS">FIG. 1</figref> in accordance with preferred embodiments of the present invention.

<figref idrefs="DRAWINGS">FIG. 2(</figref><i>e</i>) is a block diagram illustrating an alternate data structure for facts and objects in accordance with preferred embodiments of the present invention.

<figref idrefs="DRAWINGS">FIG. 3</figref> is a data flow diagram illustrating a geopoint janitor, according to one embodiment of the present invention.

<figref idrefs="DRAWINGS">FIG. 4</figref> is a flow chart illustrating a method for associating coordinates with potential place names, according to one embodiment of the present invention.

<figref idrefs="DRAWINGS">FIG. 5</figref> is an example illustrating a method for associating coordinates with potential place names, according to one embodiment of the present invention.

<figref idrefs="DRAWINGS">FIG. 6(</figref><i>a</i>) is an example illustrating a method for determining whether a text string corresponds to a potential place name, according to one embodiment of the present invention.

<figref idrefs="DRAWINGS">FIG. 6(</figref><i>b</i>) is an example illustrating a method for determining whether there are geographic location coordinates known for a potential place name, according to one embodiment of the present invention.

<figref idrefs="DRAWINGS">FIG. 7</figref> is an example illustrating a method for determining whether a text string corresponds to a potential place name, according to one embodiment of the present invention.

<figref idrefs="DRAWINGS">FIGS. 8(</figref><i>a</i>) and <b>8</b>(<i>b</i>) are examples illustrating a method for determining whether a text string corresponds to a potential place name, according to one embodiment of the present invention.

<figref idrefs="DRAWINGS">FIG. 9</figref> is an example of a bounding box that would be assigned to a place name.

<figref idrefs="DRAWINGS">FIG. 10</figref> is an example of input data in need of disambiguation.

<figref idrefs="DRAWINGS">FIG. 11</figref> is an example of a method for determining geographic location coordinates for an ambiguous potential place name.

DESCRIPTION OF EMBODIMENTS

Embodiments of the present invention are now described with reference to the figures where like reference numbers indicate identical or functionally similar elements.

<figref idrefs="DRAWINGS">FIG. 1</figref> shows a system architecture <b>100</b> adapted to support one embodiment of the invention. <figref idrefs="DRAWINGS">FIG. 1</figref> shows components used to add facts into, and retrieve facts from a repository <b>115</b>. The system architecture <b>100</b> includes a network <b>104</b>, through which any text string of document hosts <b>102</b> communicate with a data processing system <b>106</b>, along with any text string of object requesters <b>152</b>, <b>154</b>.

Document hosts <b>102</b> store documents and provide access to documents. A document is comprised of any machine-readable data including any combination of text, graphics, multimedia content, etc. One example of a document is a book (e.g., fiction or nonfiction) in machine-readable form. A document may be encoded in a markup language, such as Hypertext Markup Language (HTML), e.g., a web page, in an interpreted language (e.g., JavaScript) or in any other computer readable or executable format. A document can include one or more hyperlinks to other documents. A typical document will include one or more facts within its content. A document stored in a document host <b>102</b> may be located and/or identified by a Uniform Resource Locator (URL), or Web address, or any other appropriate form of identification and/or location. A document host <b>102</b> is implemented by a computer system, and typically includes a server adapted to communicate over the network <b>104</b> via networking protocols (e.g., TCP/IP), as well as application and presentation protocols (e.g., HTTP, HTML, SOAP, D-HTML, Java). The documents stored by a host <b>102</b> are typically held in a file directory, a database, or other data repository. A host <b>102</b> can be implemented in any computing device (e.g., from a PDA or personal computer, a workstation, mini-computer, or mainframe, to a cluster or grid of computers), as well as in any processor architecture or operating system.

<figref idrefs="DRAWINGS">FIG. 1</figref> shows components used to manage facts in a fact repository <b>115</b>. Data processing system <b>106</b> includes one or more importers <b>108</b>, one or more janitors <b>110</b>, a build engine <b>112</b>, a service engine <b>114</b>, and a fact repository <b>115</b> (also called simply a “repository”). Each of the foregoing are implemented, in one embodiment, as software modules (or programs) executed by processor <b>116</b>. Importers <b>108</b> operate to process documents received from the document hosts, read the data content of documents, and extract facts (as operationally and programmatically defined within the data processing system <b>106</b>) from such documents. The importers <b>108</b> also determine the subject or subjects with which the facts are associated, and extract such facts into individual items of data, for storage in the fact repository <b>115</b>. In one embodiment, there are different types of importers <b>108</b> for different types of documents, for example, dependent on the format or document type.

Janitors <b>110</b> operate to process facts extracted by importer <b>108</b>. This processing can include but is not limited to, data cleansing, object merging, and fact induction. In one embodiment, there are a number of different janitors <b>110</b> that perform different types of data management operations on the facts. For example, one janitor <b>110</b> may traverse some set of facts in the repository <b>115</b> to find duplicate facts (that is, facts that convey the same factual information) and merge them. Another janitor <b>110</b> may also normalize facts into standard formats. Another janitor <b>110</b> may also remove unwanted facts from repository <b>115</b>, such as facts related to pornographic content. Other types of janitors <b>110</b> may be implemented, depending on the types of data management functions desired, such as translation, compression, spelling or grammar correction, and the like.

Various janitors <b>110</b> act on facts to normalize attribute names, and values and delete duplicate and near-duplicate facts so an object does not have redundant information. For example, we might find on one page that Britney Spears' birthday is “12/2/1981” while on another page that her date of birth is “Dec. 2, 1981.” Birthday and Date of Birth might both be rewritten as Birthdate by one janitor and then another janitor might notice that 12/2/1981 and Dec. 2, 1981 are different forms of the same date. It would choose the preferred form, remove the other fact and combine the source lists for the two facts. As a result when you look at the source pages for this fact, on some you'll find an exact match of the fact and on others text that is considered to be synonymous with the fact.

Build engine <b>112</b> builds and manages the repository <b>115</b>. Service engine <b>114</b> is an interface for querying the repository <b>115</b>. Service engine <b>114</b>'s main function is to process queries, score matching objects, and return them to the caller but it is also used by janitor <b>110</b>.

Repository <b>115</b> stores factual information extracted from a plurality of documents that are located on document hosts <b>102</b>. A document from which a particular fact may be extracted is a source document (or “source”) of that particular fact. In other words, a source of a fact includes that fact (or a synonymous fact) within its contents.

Repository <b>115</b> contains one or more facts. In one embodiment, each fact is associated with exactly one object. One implementation for this association includes in each fact an object ID that uniquely identifies the object of the association. In this manner, any text string of facts may be associated with an individual object, by including the object ID for that object in the facts. In one embodiment, objects themselves are not physically stored in the repository <b>115</b>, but rather are defined by the set or group of facts with the same associated object ID, as described below. Further details about facts in repository <b>115</b> are described below, in relation to <figref idrefs="DRAWINGS">FIGS. 2(</figref><i>a</i>)-<b>2</b>(<i>d</i>).

It should be appreciated that in practice at least some of the components of the data processing system <b>106</b> will be distributed over multiple computers, communicating over a network. For example, repository <b>115</b> may be deployed over multiple servers. As another example, the janitors <b>110</b> may be located on any text string of different computers. For convenience of explanation, however, the components of the data processing system <b>106</b> are discussed as though they were implemented on a single computer.

In another embodiment, some or all of document hosts <b>102</b> are located on data processing system <b>106</b> instead of being coupled to data processing system <b>106</b> by a network. For example, importer <b>108</b> may import facts from a database that is a part of or associated with data processing system <b>106</b>.

<figref idrefs="DRAWINGS">FIG. 1</figref> also includes components to access repository <b>115</b> on behalf of one or more object requesters <b>152</b>, <b>154</b>. Object requesters are entities that request objects from repository <b>115</b>. Object requesters <b>152</b>, <b>154</b> may be understood as clients of the system <b>106</b>, and can be implemented in any computer device or architecture. As shown in <figref idrefs="DRAWINGS">FIG. 1</figref>, a first object requester <b>152</b> is located remotely from system <b>106</b>, while a second object requester <b>154</b> is located in data processing system <b>106</b>. For example, in a computer system hosting a blog, the blog may include a reference to an object whose facts are in repository <b>115</b>. An object requester <b>152</b>, such as a browser displaying the blog will access data processing system <b>106</b> so that the information of the facts associated with the object can be displayed as part of the blog web page. As a second example, janitor <b>110</b> or other entity considered to be part of data processing system <b>106</b> can function as object requester <b>154</b>, requesting the facts of objects from repository <b>115</b>.

<figref idrefs="DRAWINGS">FIG. 1</figref> shows that data processing system <b>106</b> includes a memory <b>107</b> and one or more processors <b>116</b>. Memory <b>107</b> includes importers <b>108</b>, janitors <b>110</b>, build engine <b>112</b>, service engine <b>114</b>, and requester <b>154</b>, each of which are preferably implemented as instructions stored in memory <b>107</b> and executable by processor <b>116</b>. Memory <b>107</b> also includes repository <b>115</b>. Repository <b>115</b> can be stored in a memory of one or more computer systems or in a type of memory such as a disk. <figref idrefs="DRAWINGS">FIG. 1</figref> also includes a computer readable medium <b>118</b> containing, for example, at least one of importers <b>108</b>, janitors <b>110</b>, build engine <b>112</b>, service engine <b>114</b>, requester <b>154</b>, and at least some portions of repository <b>115</b>. <figref idrefs="DRAWINGS">FIG. 1</figref> also includes one or more input/output devices <b>120</b> that allow data to be input and output to and from data processing system <b>106</b>. It will be understood that data processing system <b>106</b> preferably also includes standard software components such as operating systems and the like and further preferably includes standard hardware components not shown in the figure for clarity of example.

<figref idrefs="DRAWINGS">FIG. 2(</figref><i>a</i>) shows an example format of a data structure for facts within repository <b>115</b>, according to some embodiments of the invention. As described above, the repository <b>115</b> includes facts <b>204</b>. Each fact <b>204</b> includes a unique identifier for that fact, such as a fact ID <b>210</b>. Each fact <b>204</b> includes at least an attribute <b>212</b> and a value <b>214</b>. For example, a fact associated with an object representing George Washington may include an attribute of “date of birth” and a value of “Feb. 22, 1732.” In one embodiment, all facts are stored as alphanumeric characters since they are extracted from web pages. In another embodiment, facts also can store binary data values. Other embodiments, however, may store fact values as mixed types, or in encoded formats.

As described above, each fact is associated with an object ID <b>209</b> that identifies the object that the fact describes. Thus, each fact that is associated with a same entity (such as George Washington), will have the same object ID <b>209</b>. In one embodiment, objects are not stored as separate data entities in memory. In this embodiment, the facts associated with an object contain the same object ID, but no physical object exists. In another embodiment, objects are stored as data entities in memory, and include references (for example, pointers or IDs) to the facts associated with the object. The logical data structure of a fact can take various forms; in general, a fact is represented by a tuple that includes a fact ID, an attribute, a value, and an object ID. The storage implementation of a fact can be in any underlying physical data structure.

<figref idrefs="DRAWINGS">FIG. 2(</figref><i>b</i>) shows an example of facts having respective fact IDs of <b>10</b>, <b>20</b>, and <b>30</b> in repository <b>115</b>. Facts <b>10</b> and <b>20</b> are associated with an object identified by object ID “1.” Fact <b>10</b> has an attribute of “Name” and a value of “China.” Fact <b>20</b> has an attribute of “Category” and a value of “Country.” Thus, the object identified by object ID “1” has a name fact <b>205</b> with a value of “China” and a category fact <b>206</b> with a value of “Country.” Fact <b>30</b><b>208</b> has an attribute of “Property” and a value of “Bill Clinton was the 42nd President of the United States from 1993 to 2001.” Thus, the object identified by object ID “2” has a property fact with a fact ID of <b>30</b> and a value of “Bill Clinton was the 42nd President of the United States from 1993 to 2001.” In the illustrated embodiment, each fact has one attribute and one value. The text string of facts associated with an object is not limited; thus while only two facts are shown for the “China” object, in practice there may be dozens, even hundreds of facts associated with a given object. Also, the value fields of a fact need not be limited in size or content. For example, a fact about the economy of “China” with an attribute of “Economy” would have a value including several paragraphs of text, text strings, perhaps even tables of figures. This content can be formatted, for example, in a markup language. For example, a fact having an attribute “original html” might have a value of the original html text taken from the source web page.

Also, while the illustration of <figref idrefs="DRAWINGS">FIG. 2(</figref><i>b</i>) shows the explicit coding of object ID, fact ID, attribute, and value, in practice the content of the fact can be implicitly coded as well (e.g., the first field being the object ID, the second field being the fact ID, the third field being the attribute, and the fourth field being the value). Other fields include but are not limited to: the language used to state the fact (English, etc.), how important the fact is, the source of the fact, a confidence value for the fact, and so on.

<figref idrefs="DRAWINGS">FIG. 2(</figref><i>c</i>) shows an example object reference table <b>210</b> that is used in some embodiments. Not all embodiments include an object reference table. The object reference table <b>210</b> functions to efficiently maintain the associations between object IDs and fact IDs. In the absence of an object reference table <b>210</b>, it is also possible to find all facts for a given object ID by querying the repository to find all facts with a particular object ID. While <figref idrefs="DRAWINGS">FIGS. 2(</figref><i>b</i>) and <b>2</b>(<i>c</i>) illustrate the object reference table <b>210</b> with explicit coding of object and fact IDs, the table also may contain just the ID values themselves in column or pair-wise arrangements.

<figref idrefs="DRAWINGS">FIG. 2(</figref><i>d</i>) shows an example of a data structure for facts within repository <b>115</b>, according to some embodiments of the invention showing an extended format of facts. In this example, the fields include an object reference link <b>216</b> to another object. The object reference link <b>216</b> can be an object ID of another object in the repository <b>115</b>, or a reference to the location (e.g., table row) for the object in the object reference table <b>210</b>. The object reference link <b>216</b> allows facts to have as values other objects. For example, for an object “United States,” there may be a fact with the attribute of “president” and the value of “George W. Bush,” with “George W. Bush” being an object having its own facts in repository <b>115</b>. In some embodiments, the value field <b>214</b> stores the name of the linked object and the link <b>216</b> stores the object identifier of the linked object. Thus, this “president” fact would include the value <b>214</b> of “George W. Bush”, and object reference link <b>216</b> that contains the object ID for the for “George W. Bush” object. In some other embodiments, facts <b>204</b> do not include a link field <b>216</b> because the value <b>214</b> of a fact <b>204</b> may store a link to another object.

Each fact <b>204</b> also may include one or more metrics <b>218</b>. A metric provides an indication of the some quality of the fact. In some embodiments, the metrics include a confidence level and an importance level. The confidence level indicates the likelihood that the fact is correct. The importance level indicates the relevance of the fact to the object, compared to other facts for the same object. The importance level may optionally be viewed as a measure of how vital a fact is to an understanding of the entity or concept represented by the object.

Each fact <b>204</b> includes a list of one or more sources <b>220</b> that include the fact and from which the fact was extracted. Each source may be identified by a Uniform Resource Locator (URL), or Web address, or any other appropriate form of identification and/or location, such as a unique document identifier.

The facts illustrated in <figref idrefs="DRAWINGS">FIG. 2(</figref><i>d</i>) include an agent field <b>222</b> that identifies the importer <b>108</b> that extracted the fact. For example, the importer <b>108</b> may be a specialized importer that extracts facts from a specific source (e.g., the pages of a particular web site, or family of web sites) or type of source (e.g., web pages that present factual information in tabular form), or an importer <b>108</b> that extracts facts from free text in documents throughout the Web, and so forth.

Some embodiments include one or more specialized facts, such as a name fact <b>207</b> and a property fact <b>208</b>. A name fact <b>207</b> is a fact that conveys a name for the entity or concept represented by the object ID. A name fact <b>207</b> includes an attribute <b>224</b> of “name” and a value, which is the name of the object. For example, for an object representing the country Spain, a name fact would have the value “Spain.” A name fact <b>207</b>, being a special instance of a general fact <b>204</b>, includes the same fields as any other fact <b>204</b>; it has an attribute, a value, a fact ID, metrics, sources, etc. The attribute <b>224</b> of a name fact <b>207</b> indicates that the fact is a name fact, and the value is the actual name. The name may be a string of characters. An object ID may have one or more associated name facts, as many entities or concepts can have more than one name. For example, an object ID representing Spain may have associated name facts conveying the country's common name “Spain” and the official name “Kingdom of Spain.” As another example, an object ID representing the U.S. Patent and Trademark Office may have associated name facts conveying the agency's acronyms “PTO” and “USPTO” as well as the official name “United States Patent and Trademark Office.” If an object does have more than one associated name fact, one of the name facts may be designated as a primary name and other name facts may be designated as secondary names, either implicitly or explicitly.

A property fact <b>208</b> is a fact that conveys a statement about the entity or concept represented by the object ID. Property facts are generally used for summary information about an object. A property fact <b>208</b>, being a special instance of a general fact <b>204</b>, also includes the same parameters (such as attribute, value, fact ID, etc.) as other facts <b>204</b>. The attribute field <b>226</b> of a property fact <b>208</b> indicates that the fact is a property fact (e.g., attribute is “property”) and the value is a string of text that conveys the statement of interest. For example, for the object ID representing Bill Clinton, the value of a property fact may be the text string “Bill Clinton was the 42nd President of the United States from 1993 to 2001.” Some object IDs may have one or more associated property facts while other objects may have no associated property facts. It should be appreciated that the data structures shown in <figref idrefs="DRAWINGS">FIGS. 2(</figref><i>a</i>)-<b>2</b>(<i>d</i>) and described above are merely exemplary. The data structure of the repository <b>115</b> may take on other forms. Other fields may be included in facts and some of the fields described above may be omitted. Additionally, each object ID may have additional special facts aside from name facts and property facts, such as facts conveying a type or category (for example, person, place, movie, actor, organization, etc.) for categorizing the entity or concept represented by the object ID. In some embodiments, an object's name(s) and/or properties may be represented by special records that have a different format than the general facts records <b>204</b>.

As described previously, a collection of facts is associated with an object ID of an object. An object may become a null or empty object when facts are disassociated from the object. A null object can arise in a number of different ways. One type of null object is an object that has had all of its facts (including name facts) removed, leaving no facts associated with its object ID. Another type of null object is an object that has all of its associated facts other than name facts removed, leaving only its name fact(s). Alternatively, the object may be a null object only if all of its associated name facts are removed. A null object represents an entity or concept for which the data processing system <b>106</b> has no factual information and, as far as the data processing system <b>106</b> is concerned, does not exist. In some embodiments, facts of a null object may be left in the repository <b>115</b>, but have their object ID values cleared (or have their importance to a negative value). However, the facts of the null object are treated as if they were removed from the repository <b>115</b>. In some other embodiments, facts of null objects are physically removed from repository <b>115</b>.

<figref idrefs="DRAWINGS">FIG. 2(</figref><i>e</i>) is a block diagram illustrating an alternate data structure <b>290</b> for facts and objects in accordance with preferred embodiments of the invention. In this data structure, an object <b>290</b> contains an object ID <b>292</b> and references or points to facts <b>294</b>. Each fact includes a fact ID <b>295</b>, an attribute <b>297</b>, and a value <b>299</b>. In this embodiment, an object <b>290</b> actually exists in memory <b>107</b>.

<figref idrefs="DRAWINGS">FIG. 3</figref> is a data flow diagram illustrating a geopoint janitor <b>304</b>, according to one embodiment of the present invention. A source document <b>302</b> may be a document, such as a website. The source document <b>302</b> may also be a fact that has been extracted previously from a document and may be stored within a computer memory. For the purposes of illustration, a single source document <b>302</b> is shown in <figref idrefs="DRAWINGS">FIG. 3</figref>. In another embodiment, a plurality of source documents <b>302</b> may be used by geopoint janitor <b>304</b>.

According to one embodiment, geopoint janitor <b>304</b> determines whether at least one text string listed within source document <b>302</b> is a potential place name through the application of various rules <b>308</b>, as described below with reference to <figref idrefs="DRAWINGS">FIG. 4</figref>. Geopoint janitor <b>304</b> determines whether there are known geographic location coordinates associated with the potential place name through examining a text file <b>314</b>, existing annotated place names <b>310</b> and/or through a coordinate lookup service <b>312</b>, according to one embodiment. If such known coordinates exist, geopoint janitor <b>304</b> tags the place name with the coordinates <b>306</b>. The process of determining whether geographic location coordinates are known for the potential place name, and tagging the place name if the coordinates are known, is described below with reference to <figref idrefs="DRAWINGS">FIGS. 4-8(</figref><i>b</i>).

<figref idrefs="DRAWINGS">FIG. 4</figref> is a flow chart illustrating a method for tagging place names with geographic location coordinates, according to one embodiment of the present invention. While the method is described with reference to <figref idrefs="DRAWINGS">FIG. 4</figref> as being performed by a geopoint janitor <b>304</b> on information from one or more websites, the method is also useful in other contexts in which it is desired to identify potential place names and tag the place names with geographic location coordinates, for example, from information stored in a fact repository or other data structure or memory.

According to one embodiment, geopoint janitor <b>304</b> processes a text string to identify one or more potential place names <b>410</b>. The text string may contain multiple sentences (e.g. “I love visiting Las Vegas, as long as the trip lasts no longer than 48 hours. Also, it's best if at least two years have elapsed since my last trip.”) The text string may be only a single word (e.g. “Hawaii”).

Geopoint janitor <b>304</b> processes a text string to identify a potential place name <b>410</b> by examining whether the text string contains sequences of one or more capitalized words. For example, in the text, “I visited the Empire State Building in New York City,” geopoint janitor <b>304</b> would examine the sequences, “I”, “Empire State Building” and “New York City.” The capitalized words may be one or more capitalized letters, such as “NY” and “N.Y.” Geopoint Janitor examines the text string to identify a potential place name in accordance with various rules <b>308</b>, such as eliminating consideration of certain noise words (e.g., The, Moreover, Although, In, However, I, Mr., Ms.) or not considering the first word of a sentence. In the previous example, the first sequence, “I”, would be excluded from consideration based on rules eliminating noise words and/or the first word of a sentence. As another example of a rule <b>308</b>, geopoint janitor <b>304</b> may consider the words preceding and/or following a potential place name. For instance, words after the word “in” in the previous example would be examined because “in” often precedes a place name. Knowledge of what often precedes a place name can be learned through an iterative process. For example, “in” could be learned from the above example if the geopoint janitor <b>304</b> already knows that “New York City” is a place.

<figref idrefs="DRAWINGS">FIGS. 5 and 6(</figref><i>a</i>) illustrate how the geopoint janitor <b>304</b> can recognize variations of a potential place name, according to one embodiment. In <figref idrefs="DRAWINGS">FIG. 5</figref>, the text string depicted in value <b>214</b> has a variation of the state “California” as “Golden State” and the state “New York” as “Empire State.” The geopoint janitor <b>304</b> can recognize various representations of the same names in variety of ways, such as by examining resources within its memory or accessing a collection of information. In one embodiment, when the variations of the same place name appear in the same text string (e.g. “I love visiting the Empire State; New York is a fabulous place to vacation.”), geopoint janitor <b>304</b> can store the variations in memory for use in tagging other text strings. Examples of some of the other variations of the place names in <figref idrefs="DRAWINGS">FIG. 5</figref> are stored in a computer memory as depicted in <figref idrefs="DRAWINGS">FIG. 6(</figref><i>a</i>).

Turning now to <figref idrefs="DRAWINGS">FIG. 7</figref>, another rule <b>308</b> that the geopoint janitor <b>304</b> may use when processing a text string to identify a potential place name <b>410</b> (<figref idrefs="DRAWINGS">FIG. 4</figref>) is through examining attribute patterns for the attribute name associated with the text string. For example, a fact having as a value a text string that included the word “Turkey” would be ambiguous until the attribute name of the fact was examined. If the attribute name were “Food”, this text string would not be identified as containing a potential place name. However, if the attribute name were “Country”, the “Turkey” text string would be considered to have a potential place name. For example, the attribute value “China” <b>714</b> has an attribute name of “Name.” Name <b>712</b> is ambiguous and does not help determine whether this “China” represents a place name or not. However, the attribute name <b>716</b> for the “China” text string <b>718</b> is “Exports” (referring to formal china dishes). It is clear that this text string that has an “exports” attribute would not be a potential place name.

Further, geopoint janitor <b>304</b> could also examine object type in determining whether a text string contains potential place name. In <figref idrefs="DRAWINGS">FIG. 7</figref>, the attribute name <b>712</b> for the “China” text string depicted in value <b>714</b> is “Name.” The geopoint janitor <b>304</b> could further examine the object type <b>708</b> associated with “Object: China” <b>720</b>, where the value <b>710</b> is “Place”, to determine that the “China” text string depicted in value <b>714</b> in fact contains a place name (i.e., the name of a place is probably a place name). Therefore, the text string “China” <b>714</b>, would be considered a potential place name.

Moreover, a rule may be created that if the type of an object (such as “China”) is a place and if the attribute name for the text string at issue (associated with that object) is a name, then the text string at issue must contain a place name. This rule may be part of rules <b>308</b> (<figref idrefs="DRAWINGS">FIG. 3</figref>) to be used by Geopoint Janitor <b>304</b> in processing text strings to identify a potential place name <b>410</b> (<figref idrefs="DRAWINGS">FIG. 4</figref>).

In addition, the geopoint janitor <b>304</b> can determine which attributes are likely associated with location values. For example, if an attribute (i.e. Favorite Place) is determined to correspond to a location value more than a specified proportion of the time, geopoint janitor <b>304</b> can create a rule that all values associated with such an attribute are locations. For instance, assume the following facts were available:

Example 1A

Country: United States

Country: Russia

Country: UK

Example 1B

Favorite Place: Argentina

Favorite Place: UK

Favorite Place The White House

In Example 1A, geopoint janitor <b>304</b> might not recognize UK as a place name at first. However, after the United States and Russia were both found to be places, geopoint janitor <b>304</b> could make the determination that a “Country” attribute is a “place” and therefore determine that the UK is a place. In Example 1B, after the determination has been made that the UK is a place, and Argentina is a place, geopoint janitor <b>304</b> could make the determination that a “Favorite Place” attribute would correspond to a “place” value, so “The White House” is also likely to be a place. Geopoint janitor <b>304</b> can then use the expanded list of place-related attributes to search for additional place names.

In <figref idrefs="DRAWINGS">FIGS. 8(</figref><i>a</i>) and <b>8</b>(<i>b</i>), a second object is examined to determine whether a text string contains a potential place name. In <figref idrefs="DRAWINGS">FIG. 8(</figref><i>b</i>), the text string depicted in value <b>814</b> is “The President lives in the White House.” Geopoint janitor <b>304</b> examines the object type <b>804</b> of “Object: White House” <b>808</b>, which is “place.” Because the object type <b>804</b> of the “White House” object <b>808</b> is a place, geopoint janitor <b>304</b> recognizes that the text string “The President lives in the White House” contains the identical words, and therefore “White House” is a place name.

Returning now to <figref idrefs="DRAWINGS">FIG. 4</figref>, geopoint janitor <b>304</b> determines whether geographic location coordinates are known for the potential place name <b>420</b>. The geopoint janitor <b>304</b> makes this determination in variety of ways, such as by examining resources within its memory, for example existing annotated place names <b>310</b>, by examining a text file <b>314</b>, or by accessing a collection of information, for example a coordinate lookup service <b>312</b>.

<figref idrefs="DRAWINGS">FIGS. 5 to 6(</figref><i>b</i>) illustrate a method for determining whether geographic location coordinates are known for a potential place name <b>420</b>, according to one embodiment of the present invention. After the text string in value <b>214</b> of <figref idrefs="DRAWINGS">FIG. 5</figref> has been processed to identify potential place names, geopoint janitor <b>304</b> determines whether there are known geographic location coordinates associated with the potential place name through examining existing annotated place names <b>310</b>, by examining a text file <b>314</b>, and/or accessing a coordinate lookup service <b>312</b>, according to one embodiment. For example, in <figref idrefs="DRAWINGS">FIG. 6(</figref><i>b</i>), the geographic location coordinates for the California and New York place names are shown stored in a computer memory. A lookup function for “California,” for example, will result in the latitude and longitude (or, here, the latitude and longitude ranges) for California. One of ordinary skill in the art will recognize there are various ways of providing and accessing a lookup service in addition to those illustrated in <figref idrefs="DRAWINGS">FIGS. 6(</figref><i>a</i>) and <b>6</b>(<i>b</i>).

The lookup functions described above may yield various results. In one embodiment, a look up yields a place name with a latitude and a longitude. In another embodiment, the lookup results in the determination that the potential place name is in fact a place name, though it does not have location coordinates. Another lookup result is a place name with a bounding area <b>910</b> that has a latitude and longitude coordinate range, as shown for example in <figref idrefs="DRAWINGS">FIG. 9</figref>. In the example of a bounding area <b>910</b>, depicted for New York State, parts of Canada, the Atlantic Ocean and other states are encapsulated within that area. Although a box shape is depicted in <figref idrefs="DRAWINGS">FIG. 9</figref>, a circle, polygon, rectangle or any other shape may be used as a bounding area. A line or point may also be used as a bounding area, or a set of unconnected circles, polygons, rectangles, lines, points, or other shapes may also define a bounding area. For example, the bounding area for the “United States” object might include a rectangle to represent the continental <b>48</b> states, a circle to represent Alaska, and a triangle to represent Hawaii.

When a lookup returns conflicting results, geopoint janitor <b>304</b> provides various disambiguation techniques for resolving the differences. In one embodiment, the lookup result that occurs most frequently is the preferred result. For example, if the lookup of a “New York” string returned one geolocation of “New York City” and another of “New York State”, the preferred result would be the result that appears most frequently.

In another embodiment, geopoint janitor <b>304</b> would examine the overlap of the returned results for disambiguation. <figref idrefs="DRAWINGS">FIG. 10</figref> is an example of when different lookup results might occur and a technique for using the overlap of the results to disambiguate the returned results. In <figref idrefs="DRAWINGS">FIG. 10</figref>, the Parthenon Object <b>1020</b> has one fact with the location being Athens <b>1014</b> (from website xyz.com, for example) and another fact with the location being Greece <b>1018</b> (from website abc.com, for example). After applying the lookup to the “Athens” value <b>1014</b>, geopoint janitor <b>304</b> finds that “Athens” has two sets of potential location coordinates: one potential set of location coordinates in Georgia and another potential location coordinates in Greece. After applying the lookup to the “Greece” value <b>1018</b>, geopoint janitor <b>304</b> finds only one set of geographic location coordinates for the country of Greece. To resolve the ambiguity, geopoint janitor <b>304</b> can look in the same fact, according to one embodiment. For example, if the fact were “My favorite place to visit in Greece is Athens”, geopoint janitor <b>304</b> could determine that Athens is in Greece based on the context of the fact. In another embodiment, geopoint janitor <b>304</b> could examine other facts on this object, such as the fact “Athens, Greece” with a location attribute. Facts with a “location” attribute could be weighted more heavily in the disambiguation determination, according to one embodiment.

The geopoint janitor <b>304</b> could also look at the context of the original source document, such as a web page from which the document was extracted. For example, if the source page describes Greek history, has Greek words on it, or is from a .gr domain, the geopoint janitor <b>304</b> would select the geopoint location coordinates in Greece rather than those in Georgia.

In another embodiment, the geopoint janitor <b>304</b> determines any overlap between the potential geographic location coordinates and various location facts. As shown in <figref idrefs="DRAWINGS">FIG. 11</figref>, the boundary area for Greece <b>1110</b> overlaps with that for Athens, Greece <b>1120</b>. As such, the potential location coordinates for the Athens in Georgia can be disregarded as incorrect, and the potential location coordinates for the entire country of Greece can be disregarded as too general. In another embodiment, geopoint janitor <b>304</b> would determine if the potential geolocation coordinates overlap or are a determined distance away from coordinates for another related fact in selecting the appropriate geolocation coordinates.

Returning now to <figref idrefs="DRAWINGS">FIG. 4</figref>, geopoint janitor <b>304</b> identifies <b>430</b> the first potential place name as a place name and tags <b>440</b> the place name if the geographic location coordinates have been determined <b>440</b>. The tags may be located anywhere in the memory of the computer system. An illustration of tagging is shown in <figref idrefs="DRAWINGS">FIG. 5</figref>. For example, the potential place name of “Golden State” has been determined to be “California” from the table depicted in <figref idrefs="DRAWINGS">FIG. 6(</figref><i>a</i>), as described above, and the geographic location coordinates are obtained from the table depicted in <figref idrefs="DRAWINGS">FIG. 6(</figref><i>b</i>). The place name is then tagged, as shown in reference numeral <b>510</b>, with its respective known geographic location coordinates.

Similarly, the potential place name of “Empire State” in <figref idrefs="DRAWINGS">FIG. 5</figref> has been determined to be “New York” from the table depicted in <figref idrefs="DRAWINGS">FIG. 6(</figref><i>a</i>), and the geographic location coordinates are obtained from the table depicted in <figref idrefs="DRAWINGS">FIG. 6(</figref><i>b</i>). The place name is then tagged, as shown in reference numeral <b>520</b>, with its respective known geographic location coordinates. One of ordinary skill in the art will recognize there are various ways of tagging place names in addition to those illustrated in <figref idrefs="DRAWINGS">FIG. 5</figref>.

Reference in the specification to “one embodiment” or to “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.

Some portions of the above are presented in terms of methods and symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. A method is here, and generally, conceived to be a self-consistent sequence of steps (instructions) leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic or optical signals capable of being stored, transferred, combined, compared and otherwise manipulated. It is convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, text strings, or the like. Furthermore, it is also convenient at times, to refer to certain arrangements of steps requiring physical manipulations of physical quantities as modules or code devices, without loss of generality.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or “determining” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices.

Certain aspects of the present invention include process steps and instructions described herein in the form of a method. It should be noted that the process steps and instructions of the present invention can be embodied in software, firmware or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by a variety of operating systems.

The present invention also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. Furthermore, the computers referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.

The methods and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may also be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any references below to specific languages are provided for disclosure of enablement and best mode of the present invention.

While the invention has been particularly shown and described with reference to a preferred embodiment and several alternate embodiments, it will be understood by persons skilled in the relevant art that various changes in form and details can be made therein without departing from the spirit and scope of the invention.

Finally, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.